import streamlit as st
import pandas as pd
import time


st.header("Plotting Time Series Data")
st.markdown("Users can load their time-series data in **.csv** format and select a particular feature and plot-type.\
 Go ahead and use the sidebar on the left to upload your data files to start visualizing it!")

tab1, tab2 = st.tabs(["Main", "Demo"])

# Load and config
with st.sidebar:
    plot = st.radio("Select the kind of visualization:",('Feature collection', 'Users comparison', 'Data distribution'))
    file = st.file_uploader("Load CSV file", accept_multiple_files = False)
    if file:
        df_p = pd.read_csv(file, index_col = False)
        # df_p.index = df_p['Unnamed: 0'].tolist()
        try:
            del df_p['Unnamed: 0']
        except KeyError:
            pass
        if 'df_p' not in st.session_state:
                st.session_state['df_p'] = df_p
        st.success("Your data has been successfully loaded! 🤗")

with tab1:
    if 'df_p' in st.session_state:
        st.caption("Your uploaded data:")
        st.dataframe(st.session_state.df_p)
    elif 'df_right' in st.session_state:
        st.caption("Your data:")
        st.dataframe(st.session_state.df_right)
        if 'df_p' not in st.session_state:
                df_p = st.session_state.df_right
                st.session_state['df_p'] = df_p
        else:
                df_p = st.session_state.df_right
                st.session_state.df_p = df_p
    else:
        st.caption("Upload your data using the sidebar and select a plot-type to start :sunglasses:")
    
    df_base = st.session_state.df_p if 'df_p' in st.session_state else pd.DataFrame()
    n = len(df_base)
    col1, col2 = st.columns(2)
    
    # Prepare data
    if not df_base.empty and n:
        with col1:
            st.info(f"Your data has {n} samples.")
        slider_range = list(range(n))
        n_plot = st.slider("How many samples do you want to plot in the same graph", slider_range[0]+1, slider_range[-1]+1, 5)
        plot_it = st.button("Plot now! 📊")
        if plot_it:
            st.snow()
            feature = "relative_pupil_dilation"
            df_plot = df_base.head(n_plot)
            df_plot = [ini_list.strip('][').split(',') for ini_list in df_plot[feature]]
            df_plot = pd.DataFrame(df_plot)
            df_plot.columns = [str(column) for column in range(len(df_plot.columns))]
            if 'df_plot' not in st.session_state:
                st.session_state['df_plot'] = df_plot
    
    # Draw plot
    if 'df_plot' in st.session_state:
        with st.spinner(f"Drawing plot to visualize {plot.lower()}"):
            st.caption("Your visualization:")
            df_plot_t = df_plot.copy(deep=True).transpose()
            df_plot_t.columns = [str(column) for column in range(len(df_plot_t.columns))]
            st.line_chart(df_plot_t, y=list(df_plot_t.columns), height=450, width=600)
            st.dataframe(df_plot_t)
            with st.expander("See explanation"):
                    st.caption("The chart above shows...")
    elif df_base.empty and file:
        st.warning("Consider running outlier detection to clean your data!", icon="⚠️")
        
    st.caption(f"developer:: session_state keys: {list(st.session_state.keys())}")
    st.caption(f"what is the meaning of the x-axis, add to explanation")
    st.caption(f"add sample data demo in another tab")

# demo
with tab2:
    st.caption("demo under construction 🚧")